In order to properly maintain many systems, measurements must be taken in order to understand the system's behavior, to help optimize the system's operation, and to provide fault detection. As a system becomes larger and more dispersed, traditional measurement techniques can become more difficult to implement and more expensive to manage. Systems such as cellular telephone networks, internet monitoring, and epidemic control applications, for example, have the potential to become quite large and dispersed, thus requiring large numbers of measurements to properly manage. Traditional techniques for measuring such systems often suffer from “under-sampling”, because the cost and difficulty of installing and communicating with a sufficient number of probes is simply too high. These difficulties can become exacerbated in areas where the system is volatile. The more volatile a system is, the more measurements are generally needed in order to property understand it.
Currently, there is considerable interest in controlling the cost of managing these systems by using devices that may already present within the system as platforms for system measurement. For example, the use of customer owned telephones as mobile measuring platforms could be particularly appealing for the providers of cellular telephone networks because of the large numbers of devices in use, the services are already physically dispersed, and the devices already contain an inherent computing and communication capability. Further, many of today's cellular telephones already measure their location, characteristics of the cellular infrastructure, and have the ability to access external devices.
However, unlike a traditional measurement system that uses dedicated measurement platforms specifically placed to provide an efficient measurement pattern, a measurement system that uses the mobile devices of a consumer will have very little control over the time and place of the actual measurements. Instead of pre-selecting the time and place for data collection, such a system will likely have to accommodate the random spatial and/or temporal distribution of individual data collection events intrinsic to the unpredictable movement and usage of the customers.
This inherent volatility makes it important that systems such as these are given some ability to acquire more measurements in some areas than in others. Additional difficulties may arise because although a signal that is constant over a measurement area will need only few measurements to reconstruct, signals that are highly volatile can require a large number of measurements to provide the resolution necessary for signal reconstruction. System such as a cellular telephone network face environments where signals can have very large changes in measured signal value over comparatively small geographic areas. This inherent volatility makes it important that systems such as these be given some ability to acquire more measurements in some areas than in others.
Because any system will invariably have a limited capacity to process and store data, that system can be made more efficient if it stores only the data that is necessary to successfully manage the system. Efficient systems, therefore, typically filter out measurements in areas of low volatility, but continue to collect measurements in areas of high volatility. Thus, the system can optimize the management of its different aspects while using a minimum amount of data.